MLconf ATL 2015
2015-09-18
Atlanta, United States
as the organisers put it
EVENT SUMMARY MLconf Atalanta was our exciting event on September 19th, a block away of Georgia Tech at the Academy of Medicine. This year MLconf focused on ML platforms, tools and algorithms. We h…
| title | author | length |
|---|---|---|
| Jennifer Marsman, Principal Developer Evangelist, Microsoft | Jennifer Marsman | 31m |
Uses an Emotiv EPOC headset to capture 14‑channel EEG at 128 Hz sampling, maps signals to actions (a virtual cube moves reliably via mental command) and labels… | ||
| Maja Kabiljo, Software Engineer, Facebook Inc. | Maja Kabiljo | 21m |
A scalable matrix‑factorization pipeline on Apache Giraph implements rotational distribution of item shards to avoid sending feature vectors over every edge… | ||
| Ce Zhang, Postdoctoral Researcher, Stanford University | Ce Zhang | 21m |
Built a Caffe-compatible engine Caffe con Troll (CcT) that applies standard batching and CPU optimizations to get about 4.5x throughput versus Caffe and… | ||
| Allison Gilmore, Data Scientist, Ayasdi | Allison Gilmore | 20m |
Claims that topology reveals data shape and implements Ayasdi's Mapper pipeline: pick a real-valued lens (y or PCA coordinates), make overlapping bins, cluster… | ||
| Jorge Silva, Sr. Research Statistician Developer, SAS | Jorge Silva | 21m |
Proposes the aligned box criterion (ABC), a scalable implementation that builds data-adaptive reference distributions by recursively forming PCA-aligned… | ||
| Dale Smith, Data Scientist, Nexidia | Dale Smith | 10m |
Explains how tensors generalize matrices and that practical decompositions are sum of rank one tensors (CP) or multilinear PCA, shows libraries to run them… | ||
| Anusua Trivedi, Data Scientist at Texas Advanced Computing Center (TACC), UT Austin | Anusua Trivedi | 25m |
Built a PubMed recommender that turns articles into TF‑IDF vectors, computes cosine similarity to pick top‑3 documents for a user profile, then applies… | ||
| Pedro Domingos, Professor, University of Washington | Pedro Domingos | 49m |
Argues that five concrete algorithms — inverse deduction (symbolic induction), backpropagation (deep nets), genetic programming, Bayesian inference, and… | ||
| David Talby, SVP Engineering, Atigeo | David Talby | 22m |
Fraud detection is a classic adversarial analytics challenge: As soon as an automated system successfully learns to stop one scheme, fraudsters move on to… | ||
| Ted Dunning, Chief Application Architect, MapR | Ted Dunning | 27m |
Explains a practical recipe: compute co‑occurrence with the G test (log‑likelihood ratio), rank surprising pairs, and use those scores for recommendations… | ||
| Hank Roark, Data Scientist, H2O | Hank Roark | 20m |
Claim: IoT is about massive sensor data and practical ML wins over fancy transforms. Uses the NASA turbofan dataset (100 train, 100 test, 23 unlabeled sensors)… | ||
| Jorge A. Castañón, Data Scientist, IBM | Jorge A. Castañón | 20m |
RedRock is an iPad app built in 10 days that queries a 20‑node SoftLayer Hadoop/Spark cluster via REST and returns results in seconds. It trains word2vec +… | ||
| Raji Balasuubramaniyan, Senior Data Scientist, Manheim | Raji Balasuubramaniyan | 23m |
Manheim processes ~15 million cars a year and builds production models that segment dealers with K‑means into five groups (successful buyer, successful seller… | ||
| Jason Huang, Solutions Engineer, Qubole | Jason Huang | 16m |
Company history notes Hive co‑founders and frames cloud as separating storage from compute, claiming elasticity cuts idle clusters; cites industry numbers: 13%… | ||
| Sven Kreiss, Lead Data Scientist, Wildcard | Sven Kreiss | 24m |
A production microservice takes a URL, returns JSON-structured article fields using a pipeline built on Apache Spark for batch training, PhantomJS scraping for… | ||
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